Study Setting and Participants
The community-based cross-sectional study design was conducted from April 15-May 15, 2020 to assess the psychological impacts of COVID-19 on students who were learning in the four randomly selected private and public colleges and Universities, Namely; Dream Science and Technology College, Dandi Boru College, Unity University, and Dessie Health Science College. These higher institutions are found in South Wollo Zone, Dessie city administration. Dessie city administration is located 401Km away from the capital city of Ethiopia, Addis Ababa. The city has eight private colleges, one private University, and three public colleges which accommodate a total of 20,907 students in different fields of study.
All active students, registered for second-semester academic year, and those 16 and above years of age were included in this study. However, students who were seriously ill during the data collection period were not included in this study.
The sample size was calculated for both determinants and prevalence of psychological problems due to COVID 19 and the maximum sample size was considered for this study. Thus, the final sample size was determined using a single population proportion formula with assumptions: 5% type I error, 95% Confidence Intervals, 50% proportion since no study in Ethiopia on this problem. Finally, the researchers added 10% to compensate for the non-response of participants and the final sample size became 422.
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)
Where: n = required sample size, Zα/2 = critical value for normal distribution at 95 % confidence level (1.96), p = proportion of psychological problems, and d = 0.05 (5 % margin of error).
From a total of twelve colleges and universities found in Dessie city administration, four higher institutions (three colleges and one university) were randomly selected. The calculated sample size was proportionally allocated in each college based on the second-semester academic student number reports. To calculate the required number of participants from each college, we multiplied the total number of students actively learning in each college by the sampling fraction (n/N). The sampling fraction is approximately equal to six for all colleges. Accordingly, every 6th participants were selected using a systematic random sampling technique from each college registrar office log-book.
Study variables:
Dependent variable: the psychological impact of COVID 19 (Yes/No) that was assessed using all of Depression, Anxiety, and Stress Scales (DASS-21).
Independent variables: sociodemographic characteristics (age, residence, sex, marital status, educational level, the field of study, income, family size, religion), knowledge, attitude and practice towards the preventive measures of COVID-19.
Data Collection Tools and Procedures
The questionnaire was adopted from studies conducted before this study [19, 29-31] and modified into context. The questionnaire was developed in the English language and it consists of sociodemographic characteristics, 20-items for knowledge, eight items for attitude, and 12-items for the practice of preventive measures against COVID-19. The psychological impact was assessed by using mental health status assessment tool, DASS-21 (i.e. the Depression, Anxiety, and Stress Scales) [29]. The tools were translated into the local language (Amharic) and back to English to keep its consistency. The tool was pretested on 5% (21 participants) of samples other than selected colleges those found in Woldia town and some amendments were made based on the pretest findings. The data was collected using both phone-call and personal interviews. Phone-call was used for students who are out of Dessie town. The study participants were approached by trained health professionals who were working out of the selected colleges.
Data management and analysis
The data were cleaned, coded, and entered into Epi data version 3.1 software and exported to SPSS version 23.0 for analysis. The descriptive statistics was done and the results were presented using texts, frequency tables, figures, and median with Interquartile range (IQR).
Bivariate logistic regression analysis was done to assess the association between the dependent variable with each independent variable. The socio-demographic factors, knowledge, attitude, and practice of preventive measures against COVID-19 were the factors included in the bivariate logistic regression analysis. Thus, independent variables with a p-value of less than 0.25 were considered in the final model. Correlation between independent variables was assessed but we did not find any correlation between independent variables. The model fitness was also checked using the Hosmer-Lemeshow model fit-ness test. Finally, multivariable logistic regression analysis was done to control potential confounders and to identify the factors associated with the psychological impact of COVID-19 among students. The statistical significance level was declared at a P-value <0.05.
Operational Definitions
Psychological problem: students who were experienced all of Depression, Anxiety, and Stress Scale (DASS-21) [29] were considered as students affected by the psychological impacts of COVID-19.
Knowledge level: students who were correctly answered 70% or more (14/20) of the knowledge questions were considered as students with a good knowledge level while students who answered correctly below 70% of the knowledge questions were considered as having poor knowledge.
Attitude level: students who were correctly answered 70% or more (5.6/8) of the attitude questions were considered as students with a positive attitude while students who correctly answered below 70% of the attitude questions were considered as students with a negative attitude.
Practice level: students who were correctly answered 70% or more (8.5/12) of the practice questions were considered as students with a good practice e level while students who correctly answered below 70% of the practice questions were considered as students with a poor practice.